发表机构
Seoul National University; Yonsei University; TU Wien（首尔大学; 延世大学; 维也纳工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
POIL提出基于点的单次示教模仿学习框架，利用3D点集表示结合稳定动力系统，实现轨迹迁移与闭环执行，在仿真和真实实验中验证了跨类别、姿态和几何的迁移及抗扰动能力。
AI 中文摘要
我们提出了POIL，一个基于点的单次示教模仿学习框架，结合了稳定动力系统。虽然单次示教模仿避免了收集大量示教数据，但成功的单次示教操作不仅需要将示教轨迹迁移到新物体上，还需要在变化的场景条件、抓取配置和外部扰动下稳健地执行。POIL通过一个共享表示同时解决这两个问题：物体功能部件上的一组3D点，这些点同时用于轨迹迁移和闭环执行。通过点对应关系，可以实现从示教轨迹的单次迁移。POIL利用多模态大语言模型来锚定共享功能部件，并在视角、姿态和物体类别变化之间迁移轨迹。在执行过程中，多视角跟踪在线观察相同的点，Point-set BCSDM通过将每个点的速度投影到仅从跟踪点计算出的单一刚体扭曲上，以闭环方式驱动这些点。这将稳定动力模型从SE(3)姿态扩展到点集，无需已知的3D模型或姿态估计器。我们证明，在目标处，控制器成为经典SO(3)势上的梯度流，因此在刚体假设下，其终止阶段继承了该势的几乎全局收敛性。在仿真和真实机器人实验中，POIL将单个示教迁移到不同的物体类别、抓取姿态和目标几何形状，同时在执行过程中从外部扰动中恢复。项目页面：此https URL
英文摘要
We present POIL, a point-based one-shot imitation learning framework with stable dynamical systems. While one-shot imitation avoids collecting extensive demonstrations, successful one-shot manipulation requires not only transferring a demonstrated trajectory to a novel object but also executing it robustly under changing scene conditions, grasp configurations, and external disturbances. POIL addresses both problems through a shared representation: a set of 3D points on the object's functional part, used jointly for trajectory transfer and closed-loop execution. The one-shot transfer from the demonstrated trajectory is enabled with point correspondences. POIL grounds the shared functional part with a multi-modal large language model, and transfers the trajectory across viewpoint, pose, and object category changes. During execution, multi-view tracking observes the same points online, and Point-set BCSDM drives them in closed loop by projecting per-point velocities onto a single rigid-body twist computed from the tracked points alone. This extends stable dynamical models from an SE(3) pose to a point set without requiring a known 3D model or pose estimator. We show that at the goal the controller becomes a gradient flow on the classical SO(3) potential, so its terminal phase inherits the almost-global convergence of that potential under a rigid-object assumption. Across simulation and real-robot experiments, POIL transfers a single demonstration across object category, grasp pose, and goal geometry, while recovering from external disturbances during execution. Project page: https://sangminkim-99.github.io/poil
Comments9 pages, 11 figures, project page: https://sangminkim-99.github.io/poil/
